SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted Internet of Things (IoT) in Smart Cities: Privacy and Security in Concerns in Saudi Arabia(Saudi Digital Library, 2025) Alajmi, Abdullah; Ioan PetriInternet of Things (IoT) technologies have developed at an extremely fast pace.They are drastically changing urban infrastructure and paving the way for smart cities currently being built across the globe.However, as IoT devices weave into the backbone of critical urban sys- tems, these advancements raise pressing privacy and security concerns. In this thesis, I invest- igate the issues of implementing IoT in Saudi Arabias smart cities according to the principle of privacy and security, as per Saudi Arabias vision 2030. Saudi Arabias ambitions for cities such as Riyadh, Jeddah, and NEOM as smart hubs will capitalise on IoT to optimise city func- tions, enhance service delivery, and enhance living standards. This research provides an under- standing of privacy and security vulnerabilities associated with these integrations, and presents capabilities for enabling secure IoT ecosystems through ontology based management and feder- ated learning (FL) technologies. This studys methodology encompasses four key phases. The first phase is an employee perspective case survey of IoT privacy and security in Saudi Arabian cities. The second phase uses a natural language processing (NLP)-based social media analysis to assess public sentiments of IoT-based smart city applications. The third phase is the devel- opment of an ontology for an IoT smart city (ISCO) to structure and manage key components of IoT. The last phase is an implementation of federated long short-term memory (FLSTM) to enhance security through decentralised and privacy preserving learning. The findings show the challenges of IoT adoption in Saudi Arabia related to security and privacy pose significant barriers to adoption because employees are concerned about data misuse and lack of security protocols. Additional sentiment analysis of social media further indicates public apprehension, particularly concerning the application of IoT in traffic monitoring, safety, and environmental monitoring. Using the proposed IoT Smart City ontology, IoT components, including devices, users, and data properties, can be categorised in a secure and privacy friendly fashion, and the management of risks and regulatory compliance are more efficient. The findings also show that the FLSTM model offers 10%20% better threat detection accuracy than centralised models and is capable of adapting to variations in distribution of data, a key requirement for managing IoT data in changing urban environments. For privacy and security issues in smart cities, this thesis provides a comprehensive approach that enables scalable, flexible, and privacy resilient frame- works. Future work will expand the ontology to encompass evolving IoT ecosystems and then explore advanced FL architectures. This research aims to secure robust security and user trust to support Saudi Arabia’s pursuit of smart city excellence, towards a sustainable, secure, and user driven urban environment.44 0